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NishMath - #quantumai
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@blogs.nvidia.com
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Recent advancements in quantum computing include the launch of new supercomputers and the development of open-source frameworks. NVIDIA and AIST have collaborated to launch ABCI-Q, a supercomputing system designed for hybrid quantum-AI research. This system, powered by NVIDIA H100 GPUs and utilizing NVIDIA’s Quantum-2 InfiniBand platform, is hosted at the Global Research and Development Center for Business by Quantum-AI Technology (G-QuAT). ABCI-Q supports hybrid workloads by integrating GPU-based simulation with physical quantum processors from Fujitsu, QuEra, and OptQC, aiming to advance quantum error correction and algorithm development. It serves as a testbed for quantum-GPU workflows across various hardware modalities.
Quantum Machines has introduced QUAlibrate, an open-source calibration framework designed to significantly reduce the time required for quantum computer calibration. Calibration, a major hurdle in quantum system performance and scalability, can now be reduced from hours to minutes. QUAlibrate enables the creation, execution, and sharing of modular calibration protocols, allowing researchers to calibrate multi-qubit superconducting systems rapidly. At the Israeli Quantum Computing Center, full multi-qubit calibration was achieved in just 140 seconds using QUAlibrate. The framework is built on the QUA programming language and uses the Quantum Abstract Machine (QUAM) to model quantum hardware, featuring a graph-based calibration approach. These advancements are supported by strategic collaborations and investments in quantum technologies. SilQ Connect, a startup focusing on distributed quantum computing, has secured pre-seed funding to advance modular quantum interconnects. This funding from QV Studio, Quantacet, and Quantonation will support the development of microwave-optical quantum interconnects for scalable quantum systems. Additionally, Taiwan's National Center for High-Performance Computing is deploying a new NVIDIA-powered AI supercomputer to support research in climate science, quantum research, and the development of large language models. This initiative aims to foster cross-domain collaboration and global AI leadership. Recommended read:
References :
@medium.com
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References:
medium.com
, Peter Bendor-Samuel
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Quantum computing is rapidly advancing, bringing both immense potential and significant cybersecurity risks. The UK’s National Cyber Security Centre (NCSC) and experts across the globe are warning of a "colossal" overhaul needed in digital defenses to prepare for the quantum era. The concern is that powerful quantum computers could render current encryption methods obsolete, breaking security protocols that protect financial transactions, medical records, military communications, and blockchain technology. This urgency is underscored by the threat of "harvest now, decrypt later" attacks, where sensitive data is collected and stored for future decryption once quantum computers become powerful enough.
Across the globe, governments and organizations are scrambling to prepare for a quantum future by adopting post-quantum cryptography (PQC). PQC involves creating new encryption algorithms resistant to attacks from both classical and quantum computers. The U.S. National Institute of Standards and Technology (NIST) has already released several algorithms believed to be secure from quantum hacking. The NCSC has issued guidance, setting clear timelines for the UK’s migration to PQC, advising organizations to complete the transition by 2035. Industry leaders are also urging the U.S. Congress to reauthorize and expand the National Quantum Initiative to support research, workforce development, and a resilient supply chain. Oxford Ionics is one of the companies leading the way in quantum computing development. Oxford has released a multi-phase roadmap focused on achieving scalability and fault tolerance in their trapped-ion quantum computing platform. Their strategy includes the 'Foundation' phase, which involves deploying QPUs with 16-64 qubits with 99.99% fidelity, already operational. The second phase introduces chips with 256+ qubits and error rates as low as 10-8 via quantum error correction (QEC). The goal is to scale to over 10,000 physical qubits per chip, supporting 700+ logical qubits with minimal infrastructure change. There are also multiple bills introduced in the U.S. Congress and the state of Texas to foster the advancement of quantum technology. Recommended read:
References :
@www.aiwire.net
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References:
AIwire
, www.aiwire.net
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The Quantum Economic Development Consortium (QED-C) has released a report detailing the potential synergies between Quantum Computing (QC) and Artificial Intelligence (AI). The report, based on a workshop, highlights how these two technologies can work together to solve problems currently beyond the reach of classical computing. AI could be used to accelerate circuit design, application development, and error correction in QC. Conversely, QC offers the potential to enhance AI models by efficiently solving complex optimization and probabilistic tasks, which are infeasible for classical systems.
A hybrid approach, integrating the strengths of classical AI methods with QC algorithms, is expected to substantially reduce algorithmic complexity and improve the efficiency of computational processes and resource allocation. The report identifies key areas where this integration can yield significant benefits, including chemistry, materials science, logistics, energy, and environmental modeling. The applications could range from predicting high-impact weather events to improving the modeling of chemical reactions for pharmaceutical advancements. The report also acknowledges the necessity of cross-industry collaboration, expanded academic research, and increased federal support to advance QC + AI development. Celia Merzbacher, Executive Director of QED-C, emphasized the importance of collaboration between industry, academia, and governments to maximize the potential of these technologies. A House Science Committee hearing is scheduled to assess the progress of the National Quantum Initiative, underscoring the growing importance of quantum technologies in the U.S. Recommended read:
References :
@siliconangle.com
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References:
siliconangle.com
, thequantuminsider.com
SAS and Intel are collaborating to redefine AI architecture through optimized intelligence, moving away from a GPU-centric approach. This partnership focuses on aligning hardware and software roadmaps to deliver smarter performance, lower costs, and greater trust across various environments. Optimized intelligence allows businesses to tailor their AI infrastructure to specific use cases, which ensures efficient and ethical AI practices with human-centered design, instilling greater confidence in real-world outcomes. SAS and Intel have a 25-year relationship built around this concept, with deep investments in technical alignment to ensure hardware and software co-evolve.
SAS is integrating Intel's silicon innovations, such as AMX acceleration and Gaudi GPUs, into its Viya platform to provide cost-effective performance. This collaboration enables clients to deploy advanced models without overspending on infrastructure, with Viya demonstrating significant performance improvements on the latest Intel platforms. The company is also working with companies like Procter & Gamble and quantum hardware providers including D-Wave, IBM, and QuEra to develop hybrid quantum-classical solutions for real-world problems across industries like life sciences, finance, and manufacturing. A recent global SAS survey revealed that over 60% of business leaders are actively investing in or exploring quantum AI, although concerns remain regarding high costs, a lack of understanding, and unclear use cases. SAS aims to make quantum AI more accessible by working on pilot projects and research, providing guidance to businesses on applying quantum technologies. SAS Principal Quantum Architect Bill Wisotsky states that quantum technologies allow companies to analyze more data and achieve fast answers to complex questions, and SAS wants to simplify this research for its customers. Recommended read:
References :
Mike Watts@computational-intelligence.blogspot.com
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References:
computational-intelligence.blo
, computational-intelligence.blo
Recent developments highlight advancements in quantum computing, artificial intelligence, and cryptography. Classiq Technologies, in collaboration with Sumitomo Corporation and Mizuho-DL Financial Technology, achieved up to 95% compression of quantum circuits for Monte Carlo simulations used in financial risk analysis. This project explored the use of Classiq’s technology to generate more efficient quantum circuits for a novel quantum Monte Carlo simulation algorithm incorporating pseudo-random numbers proposed by Mizuho-DL FT, evaluating the feasibility of implementing quantum algorithms in financial applications.
Oxford researchers demonstrated a fast, 99.8% fidelity two-qubit gate using a simplified circuit design, achieving this using a modified coaxmon circuit architecture. Also, a collaborative team from JPMorganChase, Quantinuum, Argonne National Laboratory, Oak Ridge National Laboratory, and the University of Texas at Austin demonstrated a certified randomness protocol using a 56-qubit Quantinuum System Model H2 trapped-ion quantum computer. This is a major milestone for real-world quantum applications, with the certified randomness validated using over 1.1 exaflops of classical computing power, confirming the quantum system’s ability to generate entropy beyond classical reach. The 2025 IEEE International Conference on Quantum Artificial Intelligence will be held in Naples, Italy, from November 2-5, 2025, with a paper submission deadline of May 15, 2025. Vanderbilt University will host a series of workshops devoted to Groups in Geometry, Analysis and Logic starting May 28, 2025. Recommended read:
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